Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

https://github.com/TAMUCrypto/zkCNN

A GKR-based zero-knowledge proof protocol for CNN model inference.
https://github.com/TAMUCrypto/zkCNN

Last synced: 3 months ago
JSON representation

A GKR-based zero-knowledge proof protocol for CNN model inference.

Lists

README 

        
# zkCNN



## Introduction



This is the implementation of [this paper](https://eprint.iacr.org/2021/673), which is a GKR-based snark for CNN reference, containing some common CNN models such as LeNet5, vgg11 and vgg16. **Currently this version doesn't add complete zero-knowledge property.**



## Requirement



- C++14

- cmake >= 3.10

- GMP library



## Input Format



To run the program, the command is

```bash

# run_file is the name of executable file.

# in_file is the file containing the data and weight matrix, please refer to the details below.

# config_file is the file containing config (scale and zero-point) of the model. In this implementation,

#   we don't use this file because we compute the scale and zero-point directly from the data in each

#   layer, so it's okay to put an empty file here.

# ou_file is the prediction result of this picture.

# exp_result is the experiment results filled in the table. For the definition of the table header,

#   please refer to the file `src/global_var.hpp`.

# pic_cnt is the number of pictures to be predicted. For details please refer to the following section.



${run_file} ${in_file} ${config_file} ${ou_file} ${pic_cnt} > ${exp_result}

```



### The format of `in_file`



In the current code, we only allow one picture and one matrix to be put in this file. If `pic_cnt` > 1, then the code will internally duplicate the data for corresponding times. Thus to test different pictures, you might need to adjust the code of loading the input.



#### Data Part



This part is for a picture data, a vector reshaped from its original matrix by



![formula1](https://render.githubusercontent.com/render/math?math=ch_{in}%20%5Ccdot%20h\times%20w)



where ![formula2](https://render.githubusercontent.com/render/math?math=ch_{in}) is the number of channel, ![formula3](https://render.githubusercontent.com/render/math?math=h) is the height, ![formula4](https://render.githubusercontent.com/render/math?math=w) is the width.



#### Weight Part



This part is the set of parameters in the neural network, which contains



- convolution kernel of size ![formula10](https://render.githubusercontent.com/render/math?math=ch_{out}%20\times%20ch_{in}%20\times%20m%20\times%20m)



  where ![formula11](https://render.githubusercontent.com/render/math?math=ch_{out}) and ![formula12](https://render.githubusercontent.com/render/math?math=ch_{in}) are the number of output and input channels, ![formula13](https://render.githubusercontent.com/render/math?math=m) is the sideness of the kernel (here we only support square kernel).



- convolution bias of size ![formula16](https://render.githubusercontent.com/render/math?math=ch_{out}).



- fully-connected kernel of size ![formula14](https://render.githubusercontent.com/render/math?math=ch_{out}\times%20ch_{in}).



- fully-connected bias of size ![formula15](https://render.githubusercontent.com/render/math?math=ch_{out}).



### The format of `config_file`

Typically this is a file to record scale and zero-point for the data in each layer. However, in our current implementation, those are computed directly from the those data. Therefore, you can just leave it blank.



## Experiment Script

### Clone the repo

To run the code, make sure you clone with

``` bash

git clone --recurse-submodules [email protected]:TAMUCrypto/zkCNN.git

```

since the polynomial commitment is included as a submodule.



### Run a demo of LeNet5

The script to run LeNet5 model (please run the script in ``script/`` directory).

``` bash

./demo_lenet.sh

```



- The input data is in ``data/lenet5.mnist.relu.max/``.

- The experiment evaluation is ``output/single/demo-result-lenet5.txt``.

- The inference result is ``output/single/lenet5.mnist.relu.max-1-infer.csv``.



### Run a demo of vgg11

The script to run vgg11 model (please run the script in ``script/`` directory).

``` bash

./demo_vgg.sh

```



- The input data is in ``data/vgg11/``.

- The experiment evaluation is ``output/single/demo-result.txt``.

- The inference result is ``output/single/vgg11.cifar.relu-1-infer.csv``.



## Polynomial Commitment



Here we implement a [hyrax polynomial commitment](https://eprint.iacr.org/2017/1132.pdf) based on BLS12-381 elliptic curve. It is a submodule and someone who is interested can refer to this repo [hyrax-bls12-381](https://github.com/TAMUCrypto/hyrax-bls12-381).



## Reference

- [zkCNN: Zero knowledge proofs for convolutional neural network predictions and accuracy](https://doi.org/10.1145/3460120.3485379).

  Liu, T., Xie, X., & Zhang, Y. (CCS 2021).



- [Doubly-efficient zksnarks without trusted setup](https://doi.org/10.1109/SP.2018.00060). Wahby, R. S., Tzialla, I., Shelat, A., Thaler, J., & Walfish, M. (S&P 2018)



- [Hyrax](https://github.com/hyraxZK/hyraxZK.git)



- [mcl](https://github.com/herumi/mcl)